Vicuna 7B needs ~13.2 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q2_K quantization, expect ~35 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
3.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
23.1 tok/s
TTFT
8369 ms
Safe context
4K
Memory
14.7 GB / 11.5 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 32.8 tok/s | 3221 ms | 4K |
| Coding | F | Too heavy | 23.1 tok/s | 8369 ms | 4K |
| Agentic Coding | F | Too heavy | 14.8 tok/s | 19086 ms | 4K |
| Reasoning | F | Too heavy | 23.1 tok/s | 9890 ms | 4K |
| RAG | F | Too heavy | 14.8 tok/s | 23858 ms | 4K |
How Vicuna 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C50 |
Q3_K_S | 3 | 3.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaUpgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Yes, MacBook Pro M2 Pro 16GB can run Vicuna 7B at Q2_K quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 14.7 GB which exceeds available memory, but at Q2_K it needs only 13.2 GB. Expected decode speed: 35.3 tok/s.
Vicuna 7B (7B parameters) requires approximately 14.7 GB at Q4_K_M quantization. On MacBook Pro M2 Pro 16GB, it fits at Q2_K using 13.2 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M2 Pro 16GB the best fitting quantization is Q2_K, which uses 13.2 GB.
On MacBook Pro M2 Pro 16GB, Vicuna 7B achieves approximately 35.3 tokens per second decode speed with a time-to-first-token of 5482ms using Q2_K quantization.
For coding workloads, Vicuna 7B on MacBook Pro M2 Pro 16GB receives a F grade with 23.1 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, Vicuna 7B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-7b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 4.3 GB | Medium | C52 |
Q5_K_M | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | C53 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M2 Pro 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.